Dynamics of CP^1 lumps on a cylinder

The slow dynamics of topological solitons in the CP^1 sigma-model, known as lumps, can be approximated by the geodesic flow of the L^2 metric on certain moduli spaces of holomorphic maps. In the present work, we consider the dynamics of lumps on an infinite flat cylinder, and we show that in this case the approximation can be formulated naturally in terms of regular Kaehler metrics. We prove that these metrics are incomplete exactly in the multilump (interacting) case. The metric for two-lumps can be computed in closed form on certain totally geodesic submanifolds using elliptic integrals; particular geodesics are determined and discussed in terms of the dynamics of interacting lumps.Comment: 35 pages, 10 figure

Wage mobility, Job mobility and Spatial mobility in the Portuguese economy

This paper intends to analyse to what extent does a worker who, along with a job move undergoes a spatial move, gain a wage increase. For that matter, a sample of Quadros de Pessoal is used with information gathered regarding all the workers that are part of those tables, simultaneously for the years 1997 and 1998 as well as their working places. This information is initially used to carry out a bivariate analysis allowing characterizing the workers that change jobs, those who change working places and those who experience both changes. Afterwards, a wage equation is estimated, namely an Augmented Mincer Equation, taking into account both the hourly wage and the wage, making it possible to verify the influence of spatial mobility (through three levels of mobility, according to the distance between the old and new jobs) on the wage. In fact, the results of these estimations suggest that the longer the distance between the old and the new job, higher wage the moving worker will get. KEYWORDS Wage mobility, job mobility, spatial mobility, Portugal JEL Classification: J31, J61, J62, R23

Quantum Chern-Simons Vortices on a Sphere

The quantisation of the reduced first-order dynamics of the nonrelativistic model for Chern-Simons vortices introduced by Manton is studied on a sphere of given radius. We perform geometric quantisation on the moduli space of static solutions, using a Kaehler polarisation, to construct the quantum Hilbert space. Its dimension is related to the volume of the moduli space in the usual classical limit. The angular momenta associated with the rotational SO(3) symmetry of the model are determined for both the classical and the quantum systems. The results obtained are consistent with the interpretation of the solitons in the model as interacting bosonic particles

A combinatorial formula for homogeneous moments

We establish a combinatorial formula for homogeneous moments and give some examples where it can be put to use. An application to the statistical mechanics of interacting gauged vortices is discussed.Comment: 8 pages, LaTe

Gauged vortices in a background

We discuss the statistical mechanics of a gas of gauged vortices in the canonical formalism. At critical self-coupling, and for low temperatures, it has been argued that the configuration space for vortex dynamics in each topological class of the abelian Higgs model approximately truncates to a finite-dimensional moduli space with a Kaehler structure. For the case where the vortices live on a 2-sphere, we explain how localisation formulas on the moduli spaces can be used to compute explicitly the partition function of the vortex gas interacting with a background potential. The coefficients of this analytic function provide geometrical data about the Kaehler structures, the simplest of which being their symplectic volume (computed previously by Manton using an alternative argument). We use the partition function to deduce simple results on the thermodynamics of the vortex system; in particular, the average height on the sphere is computed and provides an interesting effective picture of the ground state.Comment: Final version: 22 pages, LaTeX, 1 eps figur

Finding new physics without learning about it: anomaly detection as a tool for searches at colliders

In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For this purpose, machine learning techniques are trained using Standard Model events, with the corresponding outputs being sensitive to physics beyond it. We explore three novel AD methods in HEP: Isolation Forest, Histogram-Based Outlier Detection, and Deep Support Vector Data Description; alongside the most customary Autoencoder. In order to evaluate the sensitivity of the proposed approach, predictions from specific new physics models are considered and compared to those achieved when using fully supervised deep neural networks. A comparison between shallow and deep anomaly detection techniques is also presented. Our results demonstrate the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders' data.We thank Guilherme Milhano, Maria Ramos and Guilherme Guedes for the careful reading of the manuscript and for the useful discussions. We also thank Ana Peixoto and Tiago Vale for providing the MadGraph cards used for the simulation of the beyond the Standard Model samples. We acknowledge the support from FCT Portugal, Lisboa2020, Compete2020, Portugal2020 and FEDER under project PTDC/FIS-PAR/29147/2017. The computational part of this work was supported by INCD (funded by FCT and FEDER under the project 01/SAICT/2016 nr. 022153) and by the Minho Advanced Computing Center (MACC). The Titan Xp GPU card used for the training of the Deep Neural Networks developed for this project was kindly donated by the NVIDIA Corporation

Transferability of deep learning models in searches for new physics at colliders

In this work we assess the transferability of deep learning models to detect beyond the standard model signals. For this we trained deep neural networks on three different signal models: tZ production via a flavor changing neutral current, pair production of vectorlike T-quarks via standard model gluon fusion and via a heavy gluon decay in a grid of three mass points: 1, 1.2 and 1.4 TeV. These networks were trained with t¯t, Z+jets and dibosons as the main backgrounds. Limits were derived for each signal benchmark using the inference of networks trained on each signal independently, so that we can quantify the degradation of their discriminative power across different signal processes. We determine that the limits are compatible within uncertainties for all networks trained on signals with vectorlike T-quarks, whether they are produced via heavy gluon decay or standard model gluon fusion. The network trained on flavor changing neutral current signal, while struggling the most on the other signals, still produces reasonable limits. These results indicate that deep learning models are capable of providing sensitivity in the search for new physics even if it manifests itself in models not assumed during training.We would like to thank A. Peixoto and J. Santiago foruseful discussions and help with signal generation. We alsoacknowledge the support from FCT Portugal, Lisboa2020,Compete2020, Portugal2020 and FEDER under ProjectNo. PTDC/FIS-PAR/29147/2017 and through GrantNo. PD/BD/135435/2017. The computational part of thiswork was supported by Infraestrutura Nacional deComputação Distribuída (INCD) (funded by FCT andFEDER under Project No. 01/SAICT/2016 n° 022153)and by the Minho Advanced Computing Center (MACC).The Titan Xp GPU card used for the training of the deepneural networks developed for this project was kindlydonated by the NVIDIA Corporation.info:eu-repo/semantics/publishedVersio

EcDBS1R4, an antimicrobial peptide effective against Escherichia coli with in vitro fusogenic ability

©2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/)Discovering antibiotic molecules able to hold the growing spread of antimicrobial resistance is one of the most urgent endeavors that public health must tackle. The case of Gram-negative bacterial pathogens is of special concern, as they are intrinsically resistant to many antibiotics, due to an outer membrane that constitutes an effective permeability barrier. Antimicrobial peptides (AMPs) have been pointed out as potential alternatives to conventional antibiotics, as their main mechanism of action is membrane disruption, arguably less prone to elicit resistance in pathogens. Here, we investigate the in vitro activity and selectivity of EcDBS1R4, a bioinspired AMP. To this purpose, we have used bacterial cells and model membrane systems mimicking both the inner and the outer membranes of Escherichia coli, and a variety of optical spectroscopic methodologies. EcDBS1R4 is effective against the Gram-negative E. coli, ineffective against the Gram-positive Staphylococcus aureus and noncytotoxic for human cells. EcDBS1R4 does not form stable pores in E. coli, as the peptide does not dissipate its membrane potential, suggesting an unusual mechanism of action. Interestingly, EcDBS1R4 promotes a hemi-fusion of vesicles mimicking the inner membrane of E. coli. This fusogenic ability of EcDBS1R4 requires the presence of phospholipids with a negative curvature and a negative charge. This finding suggests that EcDBS1R4 promotes a large lipid spatial reorganization able to reshape membrane curvature, with interesting biological implications herein discussed.This research was funded by Fundação para a Ciência e a Tecnologia—Ministério da Ciência, Tecnologia e Ensino Superior (FCT-MCTES, Portugal), Marie Skłodowska-Curie Research and Innovation Staff Exchange (MSCA-RISE, European Union) project INPACT (call H2020-MSCA-RISE-2014, grant agreement 644167), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil), Fundação de Amparo a Pesquisa do Distrito Federal (FAPDF, Brazil) and Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT, Brazil). M.M. and M.R.F. also acknowledge FCT-MCTES fellowships SPRH/BD/128290/2017 and SPRH/BD/100517/2014, respectively.info:eu-repo/semantics/publishedVersio

Deep learning for the classification of quenched jets

An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying deep learning techniques for this purpose. Samples of $Z+$jet events were simulated in vacuum and medium and used to train deep neural networks with the objective of discriminating between medium- and vacuum-like jets. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.Fundação para a Ciência e a Tecnologiainfo:eu-repo/semantics/publishedVersio